library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(magick, lib.loc = "/home/uhlitzf/miniconda3/lib/R/library")
library(ggpubr)
## load global vars:
source("_src/global_vars.R")
# meta_tbl
# clrs
# markers_v6
# markers_v6_super
# cell_type_super_lookup
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "\\.", " ")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "Ovarian", "Ov")
## load data --------------------------------------
## load cohort embeddings
seu_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs_pre/cells.tsv") %>%
mutate(cell_type = ifelse(cell_type == "Monocyte", "Myeloid.cell", cell_type)) %>%
mutate(cell_type = ifelse(cell_type == "Ovarian.cancer.cell", "Ov.cancer.cell", cell_type))
## load consensus data
consOV_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/consensusOV/SPECTRUM_freeze_v6_consensusOV.tsv") %>%
mutate(consensusOV = ordered(consensusOV, levels = names(clrs$consensusOV))) %>%
select(cell_id, consensusOV)
## join data
seu_tbl_full <- seu_tbl %>%
left_join(meta_tbl, by = "sample") %>%
filter(therapy == "pre-Rx", cell_type != "Other", tumor_supersite != "Unknown") %>%
rename(UMAP_1 = umap50_1, UMAP_2 = umap50_2) %>%
mutate(cell_type_super = cell_type_super_lookup[cell_type]) %>%
mutate(cell_type = ordered(str_replace_all(cell_type, "\\.", " "),
levels = names(clrs$cell_type))) %>%
mutate(sort_short_x = ifelse(sort_short == "U" & cell_type_super == "Immune",
"CD45+", ifelse(sort_short == "U" & cell_type_super == "Stromal",
"CD45-", sort_short))) %>%
left_join(consOV_tbl, by = "cell_id")
# seu_tbl_full <- seu_tbl_full %>%
# sample_n(10000)
scRNA compositions
source("_src/comp_plot.R")
# rank_by()
comp_tbl_sample <- seu_tbl_full %>%
filter(therapy == "pre-Rx", cell_type != "Other") %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature, cell_type) %>%
tally() %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature) %>%
mutate(nrel = n/sum(n)*100) %>%
mutate(label_supersite = "Site",
label_therapy = "Rx",
label_mutsig = "Signature") %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite)))) %>%
mutate(sample_id = paste(tumor_subsite, patient_id_short, therapy, sort_short_x)) %>%
group_by(sample_id) %>%
mutate(ntotal = sum(n)) %>%
filter(ntotal > 0) %>%
ungroup()
comp_tbl_consOV <- seu_tbl_full %>%
filter(therapy == "pre-Rx", cell_type != "Other") %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature, consensusOV) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature) %>%
mutate(nrel = n/sum(n)*100,
log10n = log10(n)) %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite)))) %>%
mutate(sample_id = paste(tumor_subsite, patient_id_short, therapy, sort_short_x)) %>%
ungroup
#
# g9 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Immunoreactive")
# g10 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Mesenchymal")
# g11 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Differentiated")
# g12 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Proliferative")
#
# g13 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Immunoreactive")
# g14 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Mesenchymal")
# g15 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Differentiated")
# g16 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Proliferative")
# pdf("_fig/002_cohort/002_comp_full.pdf", width = 3.5, height = 12)
# g1;g2;g3;g4;g5;g6;g7;g8;g9;g10;g11;g12;g13;g14;g15;g16
# dev.off()
cell type
Site
cell_types_immune <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Immune"]), "\\.", " ")
cell_types_stromal <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Stromal"]), "\\.", " ") %>%
str_replace_all("Ovarian", "Ov")
for(i in 1:length(cell_types_immune)){
cat('#### ', cell_types_immune[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45+"),
cell_type, cell_types_immune[i], cell_type, mutsig_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
B cell
Plasma cell
T cell
Myeloid cell
Dendritic cell
for(i in 1:length(cell_types_stromal)){
cat('#### ', cell_types_stromal[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45-"),
cell_type, cell_types_stromal[i], cell_type, mutsig_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
Endothelial cell
Fibroblast
Ov cancer cell
Signature
cell_types_immune <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Immune"]), "\\.", " ")
cell_types_stromal <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Stromal"]), "\\.", " ") %>%
str_replace_all("Ovarian", "Ov")
for(i in 1:length(cell_types_immune)){
cat('#### ', cell_types_immune[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45+"),
cell_type, cell_types_immune[i], cell_type, site_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
B cell
Plasma cell
T cell
Myeloid cell
Dendritic cell
for(i in 1:length(cell_types_stromal)){
cat('#### ', cell_types_stromal[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45-"),
cell_type, cell_types_stromal[i], cell_type, site_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
Endothelial cell
Fibroblast
Ov cancer cell
TCGA
Site
tcga_subtypes <- as.character(unique(consOV_tbl$consensusOV))
for(i in 1:length(tcga_subtypes)){
cat('#### ', tcga_subtypes[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_consOV, sort_short_x == "CD45+"),
consensusOV, tcga_subtypes[i], consensusOV, mutsig_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
Differentiated
Immunoreactive
Mesenchymal
Proliferative
for(i in 1:length(tcga_subtypes)){
cat('#### ', tcga_subtypes[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_consOV, sort_short_x == "CD45-"),
consensusOV, tcga_subtypes[i], consensusOV, mutsig_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
Differentiated
Immunoreactive
Mesenchymal
Proliferative
Signature
tcga_subtypes <- as.character(unique(consOV_tbl$consensusOV))
for(i in 1:length(tcga_subtypes)){
cat('#### ', tcga_subtypes[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_consOV, sort_short_x == "CD45+"),
consensusOV, tcga_subtypes[i], consensusOV, site_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
Differentiated
Immunoreactive
Mesenchymal
Proliferative
for(i in 1:length(tcga_subtypes)){
cat('#### ', tcga_subtypes[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_consOV, sort_short_x == "CD45-"),
consensusOV, tcga_subtypes[i], consensusOV, site_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
}
Differentiated
Immunoreactive
Mesenchymal
Proliferative
mpIF composition
## meta data for mpIF
source("_src/global_vars.R")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "\\.", " ")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "Ovarian", "Ov")
# mpif_pixel <- read_tsv("/work/shah/vazquezi/data/transfers/spectrum/results/mpif/v8/integrate/outputs/cohort_merge/patient/SPECTRUM/all/detection.tsv") %>%
# select(cell_id, compartment = Parent)
#
# mpif_cell_type <- read_tsv("/work/shah/vazquezi/data/transfers/spectrum/results/mpif/v8/integrate/outputs/cohort_merge/patient/SPECTRUM/all/cell_type_manual.tsv") %>%
# left_join(mpif_pixel, by = "cell_id") %>%
# mutate(cell_id = str_remove_all(cell_id, "CD68.TOX.PD1.PDL1.CD8.panCK_CK8-18.DAPI_|_component_data - resolution #1")) %>%
# separate(cell_id, into = c("patient_id", "tumor_subsite", "fov_id", "cell_idx"),
# sep = "_", remove = F) %>%
# mutate(fov_id = paste0(patient_id, "_", tumor_subsite, "_", fov_id),
# slide_id = paste0(patient_id, "_", tumor_subsite))
#
# ## expand for double and triple positive cells (cell type markers)
# mpif_ncell <- mpif_cell_type %>%
# # sample_n(10000) %>%
# mutate(CD68_state_log = CD68_state == "CD68+",
# CD8_state_log = CD8_state == "CD8+",
# panCK_state_log = panCK_state == "panCK+") %>%
# group_by(cell_id) %>%
# mutate(n_cells = sum(CD68_state_log, CD8_state_log, panCK_state_log)) %>%
# select(cell_id, n_cells) %>%
# deframe
#
# mpif_cell_type_expanded <- bind_rows(
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 0]),
# TOX_state == "TOX+" | PD1_state == "PD1+" | PDL1_state == "PDL1+") %>%
# mutate(cell_id = paste0(cell_id, "_0"),
# cell_type = c("Other")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 0]),
# TOX_state == "TOX-" & PD1_state == "PD1-" & PDL1_state == "PDL1-") %>%
# mutate(cell_id = paste0(cell_id, "_0"),
# cell_type = c("Unknown")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 1]),
# CD8_state == "CD8+") %>%
# mutate(cell_id = paste0(cell_id, "_1"),
# cell_type = c("CD8+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 1]),
# CD68_state == "CD68+") %>%
# mutate(cell_id = paste0(cell_id, "_2"),
# cell_type = c("CD68+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 1]),
# panCK_state == "panCK+") %>%
# mutate(cell_id = paste0(cell_id, "_3"),
# cell_type = c("panCK+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 2]),
# CD8_state == "CD8+") %>%
# mutate(cell_id = paste0(cell_id, "_1"),
# cell_type = c("CD8+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 2]),
# CD68_state == "CD68+") %>%
# mutate(cell_id = paste0(cell_id, "_2"),
# cell_type = c("CD68+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 2]),
# panCK_state == "panCK+") %>%
# mutate(cell_id = paste0(cell_id, "_3"),
# cell_type = c("panCK+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 3]),
# CD8_state == "CD8+") %>%
# mutate(cell_id = paste0(cell_id, "_1"),
# cell_type = c("CD8+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 3]),
# CD68_state == "CD68+") %>%
# mutate(cell_id = paste0(cell_id, "_2"),
# cell_type = c("CD68+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 3]),
# panCK_state == "panCK+") %>%
# mutate(cell_id = paste0(cell_id, "_3"),
# cell_type = c("panCK+"))
# )
#
# mpif_cell_state_expanded <- mpif_cell_type_expanded %>%
# mutate(cell_state = case_when(
# cell_type == "CD8+" ~ paste0(CD8_state, TOX_state, PD1_state),
# cell_type == "CD68+" ~ paste0(CD68_state, PDL1_state),
# cell_type == "panCK+" ~ paste0(panCK_state, PDL1_state),
# cell_type == "Unknown" ~ "Unknown",
# cell_type == "Other" ~ "Other"
# ))
#
# write_tsv(mpif_cell_state_expanded, "/work/shah/uhlitzf/data/SPECTRUM/mpIF/cell_type_manual_expanded.tsv")
mpif_cell_state_expanded <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/mpIF/cell_type_manual_expanded.tsv") %>%
select(cell_id, slide_id, fov_id, compartment, cell_type, cell_state, contains("state")) %>%
left_join(select(mpif_meta_tbl, slide_id, patient_id_short, tumor_supersite, tumor_subsite, therapy, sample_id, consensus_signature, tumor_megasite), by = "slide_id") %>%
na.omit() %>%
mutate(sort_short_x = compartment)
## cell state composition fov lvl
mpif_cell_state_n <- mpif_cell_state_expanded %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, fov_id, consensus_signature, cell_state, cell_type) %>%
tally() %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, fov_id, consensus_signature) %>%
mutate(nrel = n/sum(n)) %>%
ungroup()
## cell state composition slide lvl
mpif_cell_state_n_slide <- mpif_cell_state_expanded %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, cell_state, cell_type) %>%
tally() %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature) %>%
mutate(nrel = n/sum(n)) %>%
ungroup()
## cell state composition slide lvl compartment
mpif_cell_state_n_slide_compartment <- mpif_cell_state_expanded %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x, cell_state, cell_type) %>%
tally() %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup()
cell type
Site
cell_types_mpif <- c("CD8+", "CD68+", "panCK+", "Other", "Unknown")
for(i in 1:length(cell_types_mpif)){
cat('#### ', cell_types_mpif[i],' \n')
plist_t <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Tumor"),
cell_type, cell_types_mpif[i], cell_state, mutsig_box = F,
nmax = 250000)
plist_s <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Stroma"),
cell_type, cell_types_mpif[i], cell_state, mutsig_box = F,
nmax = 250000)
pt <- plot_grid(plotlist = plist_t, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
ps <- plot_grid(plotlist = plist_s, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
p <- plot_grid(pt, ps, ncol = 2)
print(p)
cat(' \n \n')
}
CD8+
CD68+
panCK+
Other
Unknown
Signature
cell_types_mpif <- c("CD8+", "CD68+", "panCK+", "Other", "Unknown")
for(i in 1:length(cell_types_mpif)){
cat('#### ', cell_types_mpif[i],' \n')
plist_t <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Tumor"),
cell_type, cell_types_mpif[i], cell_state, site_box = F,
nmax = 250000)
plist_s <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Stroma"),
cell_type, cell_types_mpif[i], cell_state, site_box = F,
nmax = 250000)
pt <- plot_grid(plotlist = plist_t, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
ps <- plot_grid(plotlist = plist_s, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
p <- plot_grid(pt, ps, ncol = 2)
print(p)
cat(' \n \n')
}
CD8+
CD68+
panCK+
Other
Unknown
cell state
Site
cell_states_mpif <- names(clrs$cell_state)
for(i in 1:length(cell_states_mpif)){
cat('#### ', cell_states_mpif[i],' \n')
plist_t <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Tumor"),
cell_state, cell_states_mpif[i], cell_state, mutsig_box = F,
nmax = 250000)
plist_s <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Stroma"),
cell_state, cell_states_mpif[i], cell_state, mutsig_box = F,
nmax = 250000)
pt <- plot_grid(plotlist = plist_t, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
ps <- plot_grid(plotlist = plist_s, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
p <- plot_grid(pt, ps, ncol = 2)
print(p)
cat(' \n \n')
}
CD8+TOX-PD1-
CD8+TOX-PD1+
CD8+TOX+PD1-
CD8+TOX+PD1+
CD68+PDL1-
CD68+PDL1+
panCK+PDL1-
panCK+PDL1+
Other
Unknown
Signature
cell_states_mpif <- names(clrs$cell_state)
for(i in 1:length(cell_states_mpif)){
cat('#### ', cell_states_mpif[i],' \n')
plist_t <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Tumor"),
cell_state, cell_states_mpif[i], cell_state, site_box = F,
nmax = 250000)
plist_s <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Stroma"),
cell_state, cell_states_mpif[i], cell_state, site_box = F,
nmax = 250000)
pt <- plot_grid(plotlist = plist_t, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
ps <- plot_grid(plotlist = plist_s, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
p <- plot_grid(pt, ps, ncol = 2)
print(p)
cat(' \n \n')
}
CD8+TOX-PD1-
CD8+TOX-PD1+
CD8+TOX+PD1-
CD8+TOX+PD1+
CD68+PDL1-
CD68+PDL1+
panCK+PDL1-
panCK+PDL1+
Other
Unknown
mpIF x scRNA correlation
# seu_cohort <- read_rds("/work/shah/isabl_data_lake/analyses/68/75/6875/RNASCP/outs/integrated_seurat_final.rds")
#
# scrna_markers <- as_tibble(cbind(cell_id = colnames(seu_cohort), FetchData(seu_cohort, c("cell_type", "sample", "CD68", "PDCD1", "CD274", "TOX", "CD8A", "CD8B", "KRT8", "KRT19")))) %>%
# mutate(CD68_state = ifelse(CD68 > 0, "CD68+", "CD68-"),
# CD8_state = ifelse(CD8A > 0 | CD8B > 0, "CD8+", "CD8-"),
# panCK_state = ifelse(KRT8 > 0 | KRT19 > 0, "panCK+", "panCK-"),
# TOX_state = ifelse(TOX > 0, "TOX+", "TOX-"),
# PD1_state = ifelse(PDCD1 > 0, "PD1+", "PD1-"),
# PDL1_state = ifelse(CD274 > 0, "PDL1+", "PDL1-"))
#
# write_tsv(scrna_markers, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/scrna_mpif_marker_expression.tsv")
scrna_markers <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/scrna_mpif_marker_expression.tsv") %>%
## remove double positives
filter(!(CD68_state == "CD68+" & CD8_state == "CD8+"),
!(CD68_state == "CD68+" & panCK_state == "panCK+"),
!(CD8_state == "CD8+" & panCK_state == "panCK+")) %>%
mutate(cell_type_sc = ifelse(CD68_state == "CD68+", "CD68+", ifelse(CD8_state == "CD8+", "CD8+", ifelse(panCK_state == "panCK+", "panCK+", "Other"))),
cell_state = case_when(
cell_type_sc == "CD8+" ~ paste0(CD8_state, TOX_state, PD1_state),
cell_type_sc == "CD68+" ~ paste0(CD68_state, PDL1_state),
cell_type_sc == "panCK+" ~ paste0(panCK_state, PDL1_state),
cell_type_sc == "Unknown" ~ "Unknown",
cell_type_sc == "Other" ~ "Other"
))
markers_pos_frac_scrna_celltype <- seu_tbl_full %>%
# select(cell_id, sample) %>%
select(cell_id, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
left_join(select(scrna_markers, cell_id, cell_type_sc, cell_state), by = "cell_id") %>%
na.omit() %>%
group_by(sample, cell_type_sc, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
tally %>%
group_by(sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
mutate(nrel = n/sum(n)*100) %>%
select(sample, cell_type_sc, n, nrel, everything()) %>%
ungroup
markers_pos_frac_scrna_cellstate <- seu_tbl_full %>%
# select(cell_id, sample) %>%
select(cell_id, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
left_join(select(scrna_markers, cell_id, cell_type_sc, cell_state), by = "cell_id") %>%
na.omit() %>%
group_by(sample, cell_state, cell_type_sc, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
tally %>%
group_by(sample, cell_type_sc, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
mutate(nrel = n/sum(n)*100) %>%
select(sample, cell_type_sc, cell_state, n, nrel, everything()) %>%
ungroup
markers_pos_frac_scrna_gene <- seu_tbl_full %>%
# select(cell_id, sample) %>%
select(cell_id, sample, cell_type, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
left_join(select(scrna_markers, -sample, -cell_type), by = "cell_id") %>%
na.omit() %>%
select(-contains("state")) %>%
mutate(cell_type = str_replace_all(cell_type, "\\.", " ")) %>%
gather(gene, value, -cell_id, -sample, -cell_type, -tumor_supersite,
-consensus_signature, -patient_id_short, -sort_short_x, -cell_type_sc) %>%
mutate(gene_state = value > 0) %>%
group_by(gene, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
mutate(n = sum(gene_state),
nrel = n/length(n)*100) %>%
ungroup %>%
distinct(gene, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x, n, nrel) %>%
select(sample, gene, n, nrel, everything())
CD45+ cell type
markers_pos_scrna_mpif <- mpif_cell_state_n_slide_compartment %>%
select(sample = sample_id, cell_type_sc = cell_type,
compartment = sort_short_x, n_mpif = n, nrel_mpif = nrel) %>%
mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45P_")) %>%
group_by(sample, cell_type_sc, compartment) %>%
summarise(n_mpif = sum(n_mpif), nrel_mpif = sum(nrel_mpif)) %>%
ungroup() %>%
left_join(markers_pos_frac_scrna_celltype, by = c("sample", "cell_type_sc")) %>%
filter(sort_short_x == "CD45+",
cell_type_sc %in% c("CD8+", "CD68+"),
compartment %in% c("Stroma", "Tumor"))
common_layers <- list(
facet_wrap(cell_type_sc~compartment, scales = "free"),
stat_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
stat_cor(aes(nrel, nrel_mpif), method = "spearman", color = "black"),
labs(x = "Fraction in scRNA (CD45+)",
y = "Fraction in mpIF"),
# coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
theme(aspect.ratio = 1)
)
p1 <- ggplot(markers_pos_scrna_mpif) +
geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
scale_color_manual(values = clrs$tumor_supersite) +
common_layers
p2 <- ggplot(markers_pos_scrna_mpif) +
geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
scale_color_manual(values = clrs$consensus_signature) +
common_layers
plot_grid(p1, p2, ncol = 2)
CD45+ cell state
markers_pos_scrna_mpif_state <- mpif_cell_state_n_slide_compartment %>%
group_by(sample_id, sort_short_x, cell_type) %>%
mutate(nrel_state = n/sum(n)*100) %>%
select(sample = sample_id, cell_type_sc = cell_type,
compartment = sort_short_x,
n_mpif = n, nrel_mpif = nrel_state, cell_state) %>%
mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45P_")) %>%
left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
filter(sort_short_x == "CD45+",
cell_state %in% c("CD8+TOX-PD1-", "CD8+TOX+PD1+", "CD68+PDL1-", "CD68+PDL1+"),
compartment %in% c("Stroma", "Tumor"))
common_layers <- list(
facet_wrap(cell_state~compartment, scales = "free", ncol = 4),
geom_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
stat_cor(aes(nrel, nrel_mpif), method = "spearman", color = "black"),
labs(x = "Fraction in scRNA (CD45+)",
y = "Fraction in mpIF"),
# coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
theme(aspect.ratio = 1)
)
p1 <- ggplot(markers_pos_scrna_mpif_state) +
geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
scale_color_manual(values = clrs$tumor_supersite) +
common_layers
p2 <- ggplot(markers_pos_scrna_mpif_state) +
geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
scale_color_manual(values = clrs$consensus_signature) +
common_layers
plot_grid(p1, p2, ncol = 1)
CD45- cell type
markers_pos_scrna_mpif_cd45n <- mpif_cell_state_n_slide_compartment %>%
select(sample = sample_id, cell_type_sc = cell_type,
compartment = sort_short_x, n_mpif = n, nrel_mpif = nrel) %>%
mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45N_")) %>%
group_by(sample, cell_type_sc, compartment) %>%
summarise(n_mpif = sum(n_mpif), nrel_mpif = sum(nrel_mpif)) %>%
ungroup() %>%
left_join(markers_pos_frac_scrna_celltype, by = c("sample", "cell_type_sc")) %>%
filter(sort_short_x == "CD45-",
cell_type_sc %in% c("panCK+"),
compartment %in% c("Stroma", "Tumor"))
common_layers <- list(
facet_wrap(cell_type_sc~compartment, scales = "free"),
stat_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
stat_cor(aes(nrel, nrel_mpif), method = "pearson", color = "black"),
labs(x = "Fraction in scRNA (CD45-)",
y = "Fraction in mpIF"),
# coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
theme(aspect.ratio = 1)
)
p1 <- ggplot(markers_pos_scrna_mpif_cd45n) +
geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
scale_color_manual(values = clrs$tumor_supersite) +
common_layers
p2 <- ggplot(markers_pos_scrna_mpif_cd45n) +
geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
scale_color_manual(values = clrs$consensus_signature) +
common_layers
plot_grid(p1, p2)
CD45- cell state
markers_pos_scrna_mpif_state_cd45n <- mpif_cell_state_n_slide_compartment %>%
group_by(sample_id, sort_short_x, cell_type) %>%
mutate(nrel_state = n/sum(n)*100) %>%
select(sample = sample_id, cell_type_sc = cell_type,
compartment = sort_short_x, n_mpif = n,
nrel_mpif = nrel_state, cell_state) %>%
mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45N_")) %>%
left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
filter(sort_short_x == "CD45-",
cell_state %in% c("panCK+PDL1-", "panCK+PDL1+"),
compartment %in% c("Stroma", "Tumor"))
common_layers <- list(
facet_wrap(cell_state~compartment, scales = "free", ncol = 4),
geom_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
stat_cor(aes(nrel, nrel_mpif), method = "pearson", color = "black"),
labs(x = "Fraction in scRNA (CD45-)",
y = "Fraction in mpIF"),
# coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
theme(aspect.ratio = 1)
)
p1 <- ggplot(markers_pos_scrna_mpif_state_cd45n) +
geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
scale_color_manual(values = clrs$tumor_supersite) +
common_layers
p2 <- ggplot(markers_pos_scrna_mpif_state_cd45n) +
geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
scale_color_manual(values = clrs$consensus_signature) +
common_layers
plot_grid(p1, p2, ncol = 1)
Main text figure
plist1 <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45-"),
cell_type, "Ov cancer cell", cell_type, mutsig_box = T)
plist1$empty <- ggdraw()
plist2 <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45+"),
cell_type, "T cell", cell_type, mutsig_box = T)
plist2$empty <- ggdraw()
plist3 <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Tumor"),
cell_type, "CD8+", cell_state, mutsig_box = T, nmax = 250000)
plist3$empty <- ggdraw()
plist4 <- default_comp_grid_list(
filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Stroma"),
cell_type, "CD8+", cell_state, mutsig_box = T, nmax = 250000)
plist4$empty <- ggdraw()
pcomp1 <- plot_grid(plotlist = plist1, ncol = 1, align = "v",
rel_heights = c(0.11, 0.11, 0.11, 0.11, 0.56, 0))
pcomp2 <- plot_grid(plotlist = plist2, ncol = 1, align = "v",
rel_heights = c(0.11, 0.11, 0.11, 0.11, 0.56, 0))
pcomp3 <- plot_grid(plotlist = plist3, ncol = 1, align = "v",
rel_heights = c(0.11, 0.11, 0.11, 0.11, 0.29, 0.25))
pcomp4 <- plot_grid(plotlist = plist4, ncol = 1, align = "v",
rel_heights = c(0.11, 0.11, 0.11, 0.11, 0.29, 0.25))
gcomp1 <- plot_grid(pcomp1, pcomp2, pcomp3, pcomp4, ncol = 4)
pcomp1 <- plot_grid(plotlist = plist1[-3], ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.16, 0.58, 0))
pcomp2 <- plot_grid(plotlist = plist2[-3], ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.16, 0.58, 0))
pcomp3 <- plot_grid(plotlist = plist3[-3], ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.16, 0.32, 0.26))
pcomp4 <- plot_grid(plotlist = plist4[-3], ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.16, 0.32, 0.26))
gcomp2 <- plot_grid(pcomp1, pcomp2, ggdraw(), pcomp3, pcomp4, ncol = 5,
rel_widths = c(0.245, 0.245, 0.02, 0.245, 0.245))
mpif_state_legend <- get_legend(plot_comp_bar(
rank_by(filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Tumor"),
cell_type, "CD8+", cell_state),
sample_id_lvl, nrel, cell_state, facet = "sort_short_x") +
scale_color_manual(values = c(Adnexa = "#ff0000", Other = "#56B4E9"), labels = c("Enriched", "Depleted", "")) +
labs(fill = "mpIF cell state") +
guides(fill = guide_legend(ncol = 2)))
vec_legend_helper <- plot_comp_vector(
rank_by(filter(comp_tbl_sample, sort_short_x == "CD45+"),
cell_type, "T cell", cell_type),
sample_id_rank, patient_id_short,
tumor_megasite, tumor_megasite, "Adnexa",
cell_type, "T cell") +
scale_color_manual(values = c(Adnexa = "#ff0000", Other = "#56B4E9"), labels = c("Enriched", "Depleted", "")) +
labs(color = "Non-adnexal\ninfiltration")
vec_legend_shape <- get_legend(vec_legend_helper + guides(color = F))
vec_legend_color <- get_legend(vec_legend_helper + guides(shape = F))
markers_pos_scrna_mpif_state_cd45p <- mpif_cell_state_n_slide_compartment %>%
group_by(sample_id, sort_short_x, cell_type) %>%
mutate(nrel_state = n/sum(n)*100) %>%
select(sample = sample_id, cell_type_sc = cell_type,
compartment = sort_short_x,
n_mpif = n, nrel_mpif = nrel_state, cell_state) %>%
mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45P_")) %>%
left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
filter(sort_short_x == "CD45+",
cell_state %in% c("CD8+TOX+PD1+", "CD68+PDL1+"),
compartment %in% c("Stroma", "Tumor"))
markers_pos_scrna_mpif_state_cd45n <- mpif_cell_state_n_slide_compartment %>%
group_by(sample_id, sort_short_x, cell_type) %>%
mutate(nrel_state = n/sum(n)*100) %>%
select(sample = sample_id, cell_type_sc = cell_type,
compartment = sort_short_x, n_mpif = n,
nrel_mpif = nrel_state, cell_state) %>%
mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45N_")) %>%
left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
filter(sort_short_x == "CD45-",
cell_state %in% c("panCK+PDL1+"),
compartment %in% c("Stroma", "Tumor"))
markers_pos_scrna_mpif_state <- bind_rows(markers_pos_scrna_mpif_state_cd45p,
markers_pos_scrna_mpif_state_cd45n) %>%
mutate(compartment = ordered(compartment, levels = c("Tumor", "Stroma"))) %>%
mutate(cell_state = case_when(
cell_state == "panCK+PDL1+" ~ "panCK+PDL1+\npanCK+",
cell_state == "CD8+TOX+PD1+" ~ "CD8+TOX+PD1+\nCD8+",
cell_state == "CD68+PDL1+" ~ "CD68+PDL1+\nCD68+"
))
common_layers <- list(
facet_grid(cell_state~compartment, scales = "free"),
geom_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
stat_cor(aes(nrel, nrel_mpif), method = "spearman", color = "black"),
labs(x = "Fraction in scRNA [%]",
y = "Fraction in mpIF [%]"),
# coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
theme(aspect.ratio = 1, plot.margin = margin(0, 0, 0, 0)),
guides(color = F)
)
cor_plot_list <- list()
cor_plot_list$p1 <- ggplot(filter(markers_pos_scrna_mpif_state,
cell_state %in% c("panCK+PDL1+\npanCK+"))) +
geom_point(aes(nrel, nrel_mpif, color = cell_type_sc)) +
scale_color_manual(values = clrs$cell_type) +
common_layers +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
strip.text.x = element_blank())
cor_plot_list$p2 <- ggplot(filter(markers_pos_scrna_mpif_state,
cell_state %in% c("CD68+PDL1+\nCD68+"))) +
geom_point(aes(nrel, nrel_mpif, color = cell_type_sc)) +
scale_color_manual(values = clrs$cell_type) +
common_layers +
theme(axis.title.x = element_blank(),
strip.text.x = element_blank())
cor_plot_list$p3 <- ggplot(filter(markers_pos_scrna_mpif_state,
cell_state %in% c("CD8+TOX+PD1+\nCD8+"))) +
geom_point(aes(nrel, nrel_mpif, color = cell_type_sc)) +
scale_color_manual(values = clrs$cell_type) +
common_layers +
theme(axis.title.y = element_blank(),
strip.text.x = element_blank())
cor_plot_grid <- plot_grid(plotlist = cor_plot_list, ncol = 1, align = "hv")
comp_grid_full <- ggdraw() +
draw_plot(gcomp2, x = 0, y = 0, width = 0.69, height = 1) +
draw_grob(vec_legend_shape, x = 0.37, y = 0.13, vjust = 0.5, hjust = 0) +
draw_grob(vec_legend_color, x = 0.47, y = 0.115, vjust = 0.5, hjust = 0) +
draw_grob(mpif_state_legend, x = 0.60, y = 0.105, vjust = 0.5, hjust = 0) +
draw_plot(cor_plot_grid, x = 0.68, y = 0.21, width = 0.35, height = 0.75) +
draw_label("Tumor", x = 0.815, y = 0.98) +
draw_label("Stroma", x = 0.91, y = 0.98)
comp_grid_full
ggsave(filename = "_fig/002_cohort/002_comp_grid_site.pdf", comp_grid_full,
width = 16, height = 8)
ggsave(filename = "_fig/002_cohort/002_comp_grid_site.png", comp_grid_full,
width = 16, height = 8)
Supplementary figure